DBMS > NCache vs. RDF4J vs. searchxml vs. Spark SQL

System Properties Comparison NCache vs. RDF4J vs. searchxml vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Open-Source and Enterprise in-memory Key-Value Store

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

DBMS for structured and unstructured content wrapped with an application server

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Key-value store

RDF store

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Search engine

Using distributed Lucene

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.alachisoft.com/ncache

rdf4j.org

www.searchxml.net/category/products

spark.apache.org/sql

Technical documentation

www.alachisoft.com/resources/docs

rdf4j.org/documentation

www.searchxml.net/support/handouts

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Alachisoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

informationpartners gmbh

Apache Software Foundation

Initial release

2005

2004

2015

2014

Current release

5.3.3, April 2024

1.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Enterprise Edition available

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C#, .NET, .NET Core, Java

Java

C++

Scala

Server operating systems

Linux
Windows

Linux
OS X
Unix
Windows

Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

yes

Typing

predefined data types such as float or date

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

SQL-like DML and DDL statements

APIs and other access methods

IDistributedCache
JCache
LINQ
Proprietary native API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC

Supported programming languages

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Java
PHP
Python

C++

most other programming languages supported via APIs

Java
Python
R
Scala

Server-side scripts

Stored procedures

support for stored procedures with SQL-Server CLR

yes

on the application server

Triggers

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

yes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with selectable consistency level

none

yes

sychronisation to multiple collections

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

optimistic locking and pessimistic locking

ACID

Isolation support depends on the API used

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Domain, group and role-based access control at the document level and for application services

More information provided by the system vendor

Specific characteristics

NCache has been the market leader in .NET Distributed Caching since 2005 . NCache...
» more

Competitive advantages

NCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
» more

Typical application scenarios

NCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
» more

Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
» more

Market metrics

Market Leader in .NET Distributed Caching since 2005.
» more

Licensing and pricing models

NCache Open Source is free on an as-is basis without any support. NCache Enterprise...
» more

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